Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New Background With appropriate algorithms, computers can learn to detect patterns and associations in large data sets. The authors’ goal was to apply machine learning to arterial pressure waveforms and create an algorithm to predict hypotension. The algorithm detects early alteration in waveforms that can herald the weakening of cardiovascular compensatory mechanisms affecting preload, afterload, and contractility. Methods The algorithm was developed with two different data sources: (1) a retrospective cohort, used for training, consisting of 1,334 patients’ records with 545,959 min of arterial waveform recording and 25,461 episodes of hypotension; and (2) a prospective, local hospital cohort used for external validation, consisting of 204 patients’ records with 33,236 min of arterial waveform recording and 1,923 episodes of hypotension. The algorithm relates a large set of features calculated from the high-fidelity arterial pressure waveform to the prediction of an upcoming hypotensive event (mean arterial pressure < 65 mmHg). Receiver-operating characteristic curve analysis evaluated the algorithm’s success in predicting hypotension, defined as mean arterial pressure less than 65 mmHg. Results Using 3,022 individual features per cardiac cycle, the algorithm predicted arterial hypotension with a sensitivity and specificity of 88% (85 to 90%) and 87% (85 to 90%) 15 min before a hypotensive event (area under the curve, 0.95 [0.94 to 0.95]); 89% (87 to 91%) and 90% (87 to 92%) 10 min before (area under the curve, 0.95 [0.95 to 0.96]); 92% (90 to 94%) and 92% (90 to 94%) 5 min before (area under the curve, 0.97 [0.97 to 0.98]). Conclusions The results demonstrate that a machine-learning algorithm can be trained, with large data sets of high-fidelity arterial waveforms, to predict hypotension in surgical patients’ records.

BACKGROUND: Intraoperative hypotension is associated with worse perioperative outcomes for patients undergoing major noncardiac surgery. The Hypotension Prediction Index is a unitless number that is derived from an arterial pressure waveform trace, and as the number increases, the risk of hypotension occurring in the near future increases. We investigated the diagnostic ability of the Hypotension Prediction Index in predicting impending intraoperative hypotension in comparison to other commonly collected perioperative hemodynamic variables. METHODS: This is a 2-center retrospective analysis of patients undergoing major surgery. Data were downloaded and analyzed from the Edwards Lifesciences EV1000 platform. Receiver operating characteristic curves were constructed for the Hypotension Prediction Index and other hemodynamic variables as well as event rates and time to event. RESULTS: Two hundred fifty-five patients undergoing major surgery were included in the analysis yielding 292,025 data points. The Hypotension Prediction Index predicted hypotension with a sensitivity and specificity of 85.8% (95% CI, 85.8%–85.9%) and 85.8% (95% CI, 85.8%–85.9%) 5 minutes before a hypotensive event (area under the curve, 0.926 [95% CI, 0.925–0.926]); 81.7% (95% CI, 81.6%–81.8%) and 81.7% (95% CI, 81.6%–81.8%) 10 minutes before a hypotensive event (area under the curve, 0.895 [95% CI, 0.894–0.895]); and 80.6% (95% CI, 80.5%–80.7%) and 80.6% (95% CI, 80.5%–80.7%) 15 minutes before a hypotensive event (area under the curve, 0.879 [95% CI, 0.879–0.880]). The Hypotension Prediction Index performed superior to all other measured hemodynamic variables including mean arterial pressure and change in mean arterial pressure over a 3-minute window. CONCLUSIONS: The Hypotension Prediction Index provides an accurate real time and continuous prediction of impending intraoperative hypotension before its occurrence and has superior predictive ability than the commonly measured perioperative hemodynamic variables.

impact of phenylephrine on cardiac output is related to preload dependency. When the heart is preload independent, phenylephrine boluses induce on average a decrease in cardiac output. When the heart is preload dependent, phenylephrine boluses induce on average an increase in cardiac output.

BackgroundMaximal left ventricular (LV) pressure rise (LV dP/dtmax), a classical marker of LV systolic function, requires LV catheterization, thus surrogate arterial pressure waveform measures have been proposed. We compared LV and arterial (femoral and radial) dP/dtmax to the slope of the LV end-systolic pressure-volume relationship (Ees), a load-independent measure of LV contractility, to determine the interactions between dP/dtmax and Ees as loading and LV contractility varied.MethodsWe measured LV pressure-volume data using a conductance catheter and femoral and radial arterial pressures using a fluid-filled catheter in 10 anesthetized pigs. Ees was calculated as the slope of the end-systolic pressure-volume relationship during a transient inferior vena cava occlusion. Afterload was assessed by the effective arterial elastance. The experimental protocol consisted of sequentially changing afterload (phenylephrine/nitroprusside), preload (bleeding/fluid bolus), and contractility (esmolol/dobutamine). A linear-mixed analysis was used to assess the contribution of cardiac (Ees, end-diastolic volume, effective arterial elastance, heart rate, preload-dependency) and arterial factors (total vascular resistance and arterial compliance) to LV and arterial dP/dtmax.ResultsBoth LV and arterial dP/dtmax allowed the tracking of Ees changes, especially during afterload and contractility changes, although arterial dP/dtmax was lower compared to LV dP/dtmax (bias 732 ± 539 mmHg⋅s− 1 for femoral dP/dtmax, and 625 ± 501 mmHg⋅s− 1 for radial dP/dtmax). Changes in cardiac contractility (Ees) were the main determinant of LV and arterial dP/dtmax changes.ConclusionAlthough arterial dP/dtmax is a complex function of central and peripheral arterial factors, radial and particularly femoral dP/dtmax allowed reasonably good tracking of LV contractility changes as loading and inotropic conditions varied.Electronic supplementary materialThe online version of this article (10.1186/s13054-018-2260-1) contains supplementary material, which is available to authorized users.